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Record W2130081213 · doi:10.1109/icnf.2011.5994302

Random telegraph signal noise in CMOS active pixel sensors

2011· article· en· W2130081213 on OpenAlexaff
M. Jamal Deen, Sumit Majumder, Ognian Marinov, Munir M. El‐Desouki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNoise (video)CMOSSIGNAL (programming language)Noise generatorNoise measurementDark-frame subtractionComputer sciencePhotodiodeFlicker noiseTransistorElectronic engineeringElectrical engineeringOptoelectronicsNoise reductionPhysicsNoise figureEngineeringArtificial intelligenceImage processingImage restorationImage (mathematics)

Abstract

fetched live from OpenAlex

We discuss the source of random telegraph signal (RTS) behavior in photodiodes, metal-oxide-semiconductor (MOS) transistors and active pixel sensors (APS). First, a detailed review on the magnitude and the time constants of RTS noise observed in state-of-the art small-pitch imagers will be presented. Second, the impact of RTS noise on the quality of the images obtained from MOS imagers will be discussed, with a focus on the noise requirements for biomedical imaging applications. Finally, our experimental results will be discussed and some ideas on how to deal with RTS noise in silicon imagers will be described based on the RTS noise analyses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.186
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2011
Admission routes1
Has abstractyes

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